The present invention relates generally to pattern recognition systems, and more specifically the invention pertains to an automatic endmember classification algorithm for hyperspectral data cubes. This algorithm has been given the name ASPIRE (Autonomous Spatial pattern Identification and REcognition algorithm).
Much of the pattern recognition data analyzed is complicated, because it often consists of overlapping spectral/spatial patterns which are difficult to separate. When studying the underlying physics and chemistry of this data, it is often necessary to first unmix (or separate) these patterns. By the end of 1997, Steve Coy's group at MIT demonstrated that their pattern recognition algorithm, Extended Cross-Correlation (XCC), can be successfully used to separate an optically thick, v=1, CO band from the other overlapping bands in the spectra. This success has motivated us to produce an in-house capability to use the XCC algorithm.
Prior art pattern recognition techniques are described in the following eight references, the disclosures of which are incorporated herein by reference:    [1] M. P. Jacobson et al. Extended cross-correlation: A technique for spectroscopic pattern recognition. J. Chem. Phys., 1997.    [2] S. L. Coy et al. Identifying patterns in multi-component signals by extended cross-correlation. J. Chem. Phys., 1997.    [3] M. P. Jacobson. Application of numerical pattern recognition to CO atmospheric simulation experiments. TBD, 1988. (work in progress).    [4] T. J. Pearson. Pgplot graphics subroutine library. http://astro.caltch.edu/˜tjp/pgplot.    [5] Interactive data language. Research System's Inc.    [6] William H. Press et al. Numerical Recipes in FORTRAN. Cambridge University Press, 1992.    [7] G. D'Agostini. A multidimensional unfolding method based on bayes' theorem. DESY 94-099, 1994.    [8] Harold J. Larson. Introduction to Probability Theory and Statistical Inference. John Wiley & sons, 1982.